Locating and tracking an object using passive sensors both indoor and outdoor have been great interests in numerous applications. For tracking an object with passive sensors, several approaches, based on time-delay estimation (TDE) methods and beamforming methods, have been proposed. The TDE method estimates location based on the time delay of arrival of signals at the receivers [1]. The beamforming method uses the frequency-averaged output power of a steered beamformer. The TDE method and beamforming method attempt to determine the current source location using data obtained at the current time only.
Each method transforms the acoustic data into a function which represents a peak in the location corresponding to the source in a deterministic way.
However, the estimation accuracy of these methods is sensitive to the noise-corrupted signals. In order to overcome the drawback of the methods, a state-space driven approach based on particle filtering was applied and proposed. The particle filtering is an emerging powerful tool for sequential signal processing, especially for nonlinear and non-Gaussian problems. The previous work on tracking with particle filters was formulated for the source localization. It presented the framework with revised TDE-based or beamforming methods using particle filtering, and the sensors are positioned at specified location at a constant height to estimate an object's trajectory in two dimensional (2-D) space. However, in those methods, the extension to three dimensional space is quite difficult and inflexible. More than the number of positioned microphones are required for generating another 2-D plane in order to extend to 3-D. In addition, mobility of the sensors cannot be supported due to their fixed position. In order to overcome the mobility problem, Direction of Arrival (DOA) based bearings-only tracking has been widely used in many applications.
In this paper, we analyze the tracking methods based on passive sensors for the flexible and accurate 3-D tracking. Tracking in 3-D has been addressed by directly extending 2-D bearings-only tracking problem to 3-D problem. Instead of directly extending traditional particle filtering algorithms for bearings-only tracking for 3-D space, we propose to decompose the 3-D particle filter into several simpler particle filters designed for 2-D bearings-only tracking problems. The decomposition and selection for the 2-D particle filters are based on the characterization of the acoustic sensor operation under noisy environment. As the passive acoustic localizer model, there is used a passive acoustic localizer proposed in M. Stanacevic, G. Cauwenberghs, “Micropower Gradient Flow acoustic Localizer,” in Solid-State Circuits Conf. (ESSCIRC03), pp. 69-72, 2003. The acoustic localizer detects two angle components (azimuth angle θ, elevation angle φ) between a sensor and an object. We extend the approach to multiple particle filter fusion for robust performance. We compare the proposed approach with the directly extended bearings-only tracking method using Cramer-Rao Low Bound.